R Notebook
Loading packages
Pulling the most recent data from covidtracking.com…
## [1] "Retrieved 2020-10-29 23:59:28"## [1] "Data as of 2020-10-29"Narrowing data…
Adding state population data…
Building tables…
done
Tests & cases
my_data %>%
inner_join(code_names, by = "code") %>%
inner_join(st_pop_2019, by = "name") %>%
filter(as_of >= Sys.Date() - weeks(2)) %>%
mutate(week_num = lubridate::week(as_of)) %>%
group_by(code) %>%
summarize(pos_rate = sum(case_ch) / sum(test_ch), reg_2 = min(reg_2)) %>%
mutate(pos_rate = if_else(pos_rate < 0, 0, pos_rate)) %>%
filter(pos_rate < 0.05 | pos_rate > 0.10) %>%
ggplot(mapping = aes(x = reorder(code, pos_rate), y = pos_rate, fill = reg_2)) +
geom_col() +
geom_label(mapping = aes(label = code)) +
labs(x = "Date", y = "Share of COVID-19 tests with positive results", title = "Positive rates remain high in the Midwest and plains", subtitle = str_c("Two weeks ending ", max(my_data$as_of))) +
theme(legend.title = element_blank(), axis.title.x = element_blank(), axis.text.x = element_blank(), axis.ticks.x = element_blank())## `summarise()` ungrouping output (override with `.groups` argument)
Tests & cases
my_data %>%
inner_join(code_names, by = "code") %>%
inner_join(st_pop_2019, by = "name") %>%
filter(as_of >= Sys.Date() - days(34)) %>%
mutate(case_days = if_else(between(as_of, Sys.Date() - days(34), Sys.Date() - days(14)), 1, 0),
death_days = if_else(as_of >= Sys.Date() - days(14), 1, 0)) %>%
group_by(code) %>%
summarize(cfr = sum(death_ch * death_days) / sum(case_ch * case_days), reg_2 = min(reg_2)) %>%
ggplot(mapping = aes(x = reorder(code, cfr), y = cfr, fill = reg_2)) +
geom_col() +
geom_label(mapping = aes(label = code)) +
labs(x = "Date", y = "Deaths as a share of confirmed cases", title = "Case fatality rate") +
theme(legend.title = element_blank(), axis.title.x = element_blank(), axis.text.x = element_blank(), axis.ticks.x = element_blank())## `summarise()` ungrouping output (override with `.groups` argument)
Tests & cases
my_data %>%
inner_join(code_names, by = "code") %>%
inner_join(st_pop_2019, by = "name") %>%
filter(as_of >= Sys.Date() - weeks(4)) %>%
mutate(week_num = lubridate::week(as_of)) %>%
group_by(week_num, reg_2) %>%
summarize(as_of = median(as_of), pos_rate = sum(case_ch) / sum(test_ch)) %>%
ggplot(mapping = aes(x = as_of, y = pos_rate, color = reg_2, fill = reg_2)) +
geom_smooth(alpha = .05, span = .5) +
labs(x = "Date", y = "Share of COVID-19 tests with positive results", title = "Positive rates are trending downward", subtitle = str_c("Two months ending ", max(my_data$as_of))) +
theme(legend.title = element_blank())## `summarise()` regrouping output by 'week_num' (override with `.groups` argument)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Tests & cases
my_data %>%
inner_join(code_names, by = "code") %>%
inner_join(st_pop_2019, by = "name") %>%
filter(as_of >= Sys.Date() - months(2)) %>%
mutate(week_num = lubridate::week(as_of)) %>%
group_by(week_num, reg_2) %>%
summarize(as_of = median(as_of), pos_rate = sum(case_ch) / sum(test_ch), wk_test = sum(test_ch)) %>%
ggplot(mapping = aes(x = as_of, y = pos_rate, color = reg_2, fill = reg_2, alpha = wk_test)) +
geom_smooth(span = .5) +
geom_line() +
labs(x = "Date", y = "Share of COVID-19 tests with positive results", title = "Positive rates remain high in the South and Southwest", subtitle = str_c("Two months ending ", max(my_data$as_of))) +
theme(legend.title = element_blank())## `summarise()` regrouping output by 'week_num' (override with `.groups` argument)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Tests & cases
my_data %>%
inner_join(code_names, by = "code") %>%
inner_join(st_pop_2019, by = "name") %>%
filter(as_of >= Sys.Date() - months(1)) %>%
mutate(week_num = lubridate::week(as_of)) %>%
group_by(week_num, code) %>%
summarize(pos_rate = sum(case_ch) / sum(test_ch),
as_of = median(as_of),
reg_2 = mode(reg_2)) %>%
group_by(code, week_num) %>%
mutate(y_label = if_else(as_of == median(as_of, na.rm = TRUE),
median(pos_rate, na.rm = TRUE),
NULL),
month_pos = mean(pos_rate, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(mapping = aes(x = as_of, y = pos_rate)) +
geom_line(alpha = 0, span = .6, mapping = aes(color = reg_2)) +
geom_label(mapping = aes(y = y_label, label = code), na.rm = TRUE) +
labs(x = "Date", y = "Share of COVID-19 tests with positive results", title = "Positive rates remain high in some states", subtitle = str_c(Sys.Date() - months(1), " through ", max(my_data$as_of), "; Ordered by highest positive rate for the month"))## `summarise()` regrouping output by 'week_num' (override with `.groups` argument)
# +
# theme(legend.title = element_blank()) +
# facet_wrap(facets = vars(reorder(code, desc(month_pos))), nrow = 5) +
# theme(axis.text = element_blank(),
# axis.ticks = element_blank(),
# axis.title.x = element_blank(),
# strip.text.x = element_blank(),
# legend.position = 0)
Adds 2016 presidential election results
results_2016 <- read_csv("results_2016.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## code = col_character(),
## t_mgn = col_double()
## )
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
mutate(noreast = if_else(code %in% (pk), "Northeastern states", "All other states")) %>%
group_by(noreast, as_of) %>%
summarize(us_cases = sum(cases, na.rm = TRUE) / sum(pop_2019, na.rm = TRUE),
us_cases_ch = sum(case_ch, na.rm = TRUE) / sum(pop_2019, na.rm = TRUE),
us_deaths = sum(death, na.rm = TRUE) / sum(pop_2019, na.rm = TRUE),
us_deaths_ch = sum(death_ch, na.rm = TRUE) / sum(pop_2019, na.rm = TRUE)) %>%
filter(us_cases > .000009) %>%
mutate(since = min(as_of)) %>%
ggplot(mapping = aes(x = as_of, y = us_deaths_ch)) +
geom_smooth(linetype = 0, alpha = .05, mapping = aes(fill = weekdays(as_of), color = noreast)) +
geom_smooth(alpha = 0, size = 1, linetype = 2, mapping = aes(color = noreast)) +
geom_smooth(color = "black") +
theme(legend.position = "none") +
labs(title = str_c(str_c(pk, collapse = ", "), " have switched w/others", sep = ""), subtitle = "Deaths per million since 10 cases per million", x = "date", y = "COVID-19 deaths")## Joining, by = "code"## Joining, by = "name"## `summarise()` regrouping output by 'noreast' (override with `.groups` argument)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
pop_dens <- read_csv("pop_density.csv") %>%
select(code, dens = Density, pop = Pop)##
## -- Column specification --------------------------------------------------------
## cols(
## State = col_character(),
## code = col_character(),
## Density = col_double(),
## Pop = col_double(),
## LandArea = col_double()
## )my_data %>%
inner_join(results_2016) %>%
inner_join(pop_dens) %>%
group_by(code) %>%
filter(as_of == max(as_of)) %>%
mutate(hrc = (t_mgn < 0),
tier = case_when(dens >= 800 ~ "F",
between(dens, 300, 800) ~ "E",
between(dens, 110, 300) ~ "D",
between(dens, 40, 110) ~ "C",
between(dens, 15, 40) ~ "B",
dens < 15 ~ "A")) %>%
ungroup() %>%
mutate(death_pm = death/pop * 1000000) %>%
ggplot(mapping = aes(x = dens, y = log(death_pm))) +
# , color = hrc, fill = hrc)) +
geom_label(mapping = aes(x = dens, y = log(death_pm), label = code, fill = NULL, color = hrc)) +
# geom_smooth(linetype = 3, method = "lm", alpha = 0.1) +
geom_abline(slope = 2.2 / 1200, intercept = 5.6, linetype = 3) +
facet_wrap(vars(tier), scales = "free_x", nrow = 2) +
labs(x = "Population per square mile", y = "COVID-19 deaths per million") +
theme(legend.position = "none") +
ylim(0, 8)## Joining, by = "code"
## Joining, by = "code"
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
mutate(day_of_week = weekdays(as_of)) %>%
group_by(code, day_of_week) %>%
mutate(max_case_ch = max(case_ch),
hi_date = if_else(case_ch == max_case_ch, as_of, NULL),
case_ch_pm = as.integer(case_ch / pop_2019 * 1000000),
pos_rate = case_ch / test_ch) %>%
filter(hi_date == max(as_of)) %>%
select(as_of, code, case_ch_pm, pos_rate) %>%
arrange(desc(case_ch_pm)) %>%
group_by(code) %>%
summarize(as_of = max(as_of), case_ch_pm = mean(case_ch_pm, na.rm = TRUE), pos_rate = max(pos_rate, na.rm = TRUE)) %>%
ggplot(mapping = aes(x = reorder(code, desc(case_ch_pm)), y = case_ch_pm, fill = pos_rate)) +
labs(title = str_c("States with a record number of new cases week ending ", max(my_data$as_of)),
y = "Daily new cases per million",
x = "State") +
geom_col() +
geom_label(mapping = aes(label = weekdays(as_of, abbreviate = TRUE)), color = "white") +
theme(axis.text.x = element_text(angle = 90, size = 8))## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"## Adding missing grouping variables: `day_of_week`## `summarise()` ungrouping output (override with `.groups` argument)
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
mutate(day_of_week = weekdays(as_of)) %>%
group_by(code, day_of_week) %>%
mutate(max_case_ch = max(case_ch),
hi_date = if_else(case_ch == max_case_ch, as_of, NULL),
case_ch_pm = as.integer(case_ch / pop_2019 * 1000000),
pos_rate = case_ch / test_ch,
is_hrc = (t_mgn < 0)) %>%
filter(hi_date == max(as_of)) %>%
select(as_of, code, case_ch_pm, pos_rate, is_hrc) %>%
arrange(desc(case_ch_pm)) %>%
group_by(code, is_hrc) %>%
summarize(as_of = max(as_of),
case_ch_pm = mean(case_ch_pm, na.rm = TRUE),
pos_rate = max(pos_rate, na.rm = TRUE)) %>%
ggplot(mapping = aes(x = reorder(code, desc(case_ch_pm)), y = case_ch_pm, fill = is_hrc)) +
labs(title = str_c("States with a record number of new cases week ending ", max(my_data$as_of)),
y = "Daily new cases per million",
x = "State") +
geom_col(color = "white") +
geom_text(mapping = aes(label = weekdays(as_of, abbreviate = TRUE)), color = "black", angle = 90) +
theme(axis.text.x = element_text(angle = 90, size = 8))## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"## Adding missing grouping variables: `day_of_week`## `summarise()` regrouping output by 'code' (override with `.groups` argument)
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
mutate(day_of_week = weekdays(as_of)) %>%
group_by(code, day_of_week, t_mgn) %>%
mutate(max_case_ch = max(case_ch),
hi_date = if_else(case_ch == max_case_ch, as_of, NULL),
case_ch_pm = as.integer(case_ch / pop_2019 * 1000000),
pos_rate = case_ch / test_ch,
is_hrc = (t_mgn < 0)) %>%
filter(hi_date == max(as_of)) %>%
select(as_of, code, case_ch_pm, pos_rate, is_hrc) %>%
arrange(desc(case_ch_pm)) %>%
group_by(code, is_hrc) %>%
summarize(as_of = max(as_of), case_ch_pm = mean(case_ch_pm, na.rm = TRUE), pos_rate = max(pos_rate, na.rm = TRUE)) %>%
ggplot(mapping = aes(x = reorder(code, desc(case_ch_pm)), y = case_ch_pm, fill = is_hrc)) +
labs(title = str_c("States with a record number of new cases week ending ", max(my_data$as_of)),
y = "Daily new cases per million",
x = "State") +
geom_col() +
geom_label(mapping = aes(label = weekdays(as_of, abbreviate = TRUE)), color = "white")## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"## Adding missing grouping variables: `day_of_week`, `t_mgn`## `summarise()` regrouping output by 'code' (override with `.groups` argument)
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
mutate(day_of_week = weekdays(as_of)) %>%
group_by(code, day_of_week) %>%
mutate(max_death_ch = max(death_ch),
hi_date = if_else(death_ch == max_death_ch, as_of, NULL),
death_ch_pm = as.integer(death_ch / pop_2019 * 1000000),
cfr = death_ch / case_ch,
is_hrc = (t_mgn < 0)) %>%
filter(hi_date == max(as_of)) %>%
select(as_of, code, death_ch_pm, cfr, is_hrc) %>%
arrange(desc(death_ch_pm)) %>%
group_by(code, is_hrc) %>%
summarize(as_of = max(as_of), death_ch_pm = mean(death_ch_pm, na.rm = TRUE), cfr = max(cfr, na.rm = TRUE)) %>%
ggplot(mapping = aes(x = reorder(code, desc(death_ch_pm)), y = death_ch_pm, fill = is_hrc)) +
labs(title = str_c("States with a record number of deaths week ending ", max(my_data$as_of)),
y = "Daily deaths per million",
x = "State") +
geom_col() +
geom_label(mapping = aes(label = weekdays(as_of, abbreviate = TRUE)), color = "white")## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"## Adding missing grouping variables: `day_of_week`## `summarise()` regrouping output by 'code' (override with `.groups` argument)
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
mutate(year_mo = month(as_of)) %>%
group_by(year_mo, code) %>%
summarize(case_ch_pm = 30.5 * mean(case_ch / pop_2019 * 1000000, na.rm = TRUE),
death_ch_pm = 30.5 * mean(death_ch / pop_2019 * 1000000, na.rm = TRUE),
is_hrc = sum(t_mgn, na.rm = TRUE) < 0,
pos_rate = sum(case_ch, na.rm = TRUE) / sum(test_ch, na.rm = TRUE),
mo_of = median(ymd(str_c("2020", year_mo, "15", sep = "-")))) %>%
filter(case_ch_pm > 0, death_ch_pm > 0) %>%
group_by(year_mo) %>%
top_n(n = 10, case_ch_pm) %>%
ggplot(mapping = aes(x = mo_of, y = case_ch_pm, label = code, color = is_hrc, size = pos_rate)) +
geom_label_repel(direction = "y", segment.color = NA) +
theme(legend.position = "none") +
scale_size(range = c(3, 6)) +
labs(x = "month",
y = "monthly cases per million",
title = "Since June, red states have led in new cases per capita")## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"## `summarise()` regrouping output by 'year_mo' (override with `.groups` argument)
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
mutate(year_mo = month(as_of)) %>%
group_by(year_mo, code) %>%
summarize(case_ch_pm = 30.5 * mean(case_ch / pop_2019 * 1000000, na.rm = TRUE),
death_ch_pm = 30.5 * mean(death_ch / pop_2019 * 1000000, na.rm = TRUE),
is_hrc = sum(t_mgn, na.rm = TRUE) < 0,
pos_rate = sum(case_ch, na.rm = TRUE) / sum(test_ch, na.rm = TRUE),
mo_of = median(ymd(str_c("2020", year_mo, "1", sep = "-")))) %>%
filter(case_ch_pm > 0, death_ch_pm > 0) %>%
group_by(year_mo) %>%
top_n(n = 10, death_ch_pm) %>%
ggplot(mapping = aes(x = mo_of, y = death_ch_pm, label = code, color = is_hrc)) +
geom_label(position = position_jitter(width = 10, height = 60), segment.color = NA) +
theme(legend.position = "none") +
labs(title = "Ten states with most per capita COVID-19 deaths by month",
y = "Monthly COVID-19 deaths per million residents",
x = "Date")## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"## `summarise()` regrouping output by 'year_mo' (override with `.groups` argument)## Warning: Ignoring unknown parameters: segment.colour
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
mutate(year_wk = week(as_of)) %>%
group_by(year_wk, code) %>%
summarize(case_ch_pm = 7 * mean(case_ch / pop_2019 * 1000000, na.rm = TRUE),
death_ch_pm = 7 * mean(death_ch / pop_2019 * 1000000, na.rm = TRUE),
is_hrc = sum(t_mgn, na.rm = TRUE) < 0,
pos_rate = sum(case_ch, na.rm = TRUE) / sum(test_ch, na.rm = TRUE),
wk_date = min(as_of, na.rm = TRUE)) %>%
filter(case_ch_pm > 0, death_ch_pm > 0) %>%
group_by(wk_date) %>%
top_n(n = 3, death_ch_pm) %>%
ggplot(mapping = aes(x = wk_date, y = death_ch_pm, label = code, fill = is_hrc)) +
geom_label(na.rm = TRUE, position = position_jitter(width = 3, height = 25)) +
theme(legend.position = "none") +
labs(subtitle = str_c("Five states with most per capita COVID-19 deaths by week through", max(my_data$as_of, na.rm = TRUE), sep = " "),
y = "Weekly COVID-19 deaths per million residents",
x = "Date") +
xlim(ymd(20200301), today())## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"## `summarise()` regrouping output by 'year_wk' (override with `.groups` argument)
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
mutate(year_wk = week(as_of)) %>%
group_by(year_wk, code) %>%
summarize(case_ch_pm = 7 * mean(case_ch / pop_2019 * 1000000, na.rm = TRUE),
death_ch_pm = 7 * mean(death_ch / pop_2019 * 1000000, na.rm = TRUE),
is_hrc = sum(t_mgn, na.rm = TRUE) < 0,
pos_rate = sum(case_ch, na.rm = TRUE) / sum(test_ch, na.rm = TRUE),
wk_date = min(as_of, na.rm = TRUE)) %>%
filter(case_ch_pm > 0, death_ch_pm > 0) %>%
group_by(wk_date) %>%
top_n(n = 5, case_ch_pm) %>%
ggplot(mapping = aes(x = wk_date, y = case_ch_pm, label = code, fill = is_hrc)) +
geom_label(na.rm = TRUE, position = position_jitter(width = 3, height = 25)) +
theme(legend.position = "none") +
labs(subtitle = str_c("Five states with most new per capita COVID-19 cases by week through", max(my_data$as_of, na.rm = TRUE), sep = " "),
y = "Weekly new COVID-19 cases per million residents",
x = "Date") +
xlim(ymd(20200301), today())## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"## `summarise()` regrouping output by 'year_wk' (override with `.groups` argument)
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
inner_join(pop_dens) %>%
mutate(year_wk = week(as_of)) %>%
group_by(year_wk, reg_2, code, dens) %>%
summarize(case_ch_pm = 7 * mean(case_ch / pop_2019 * 1000000, na.rm = TRUE),
death_ch_pm = 7 * mean(death_ch / pop_2019 * 1000000, na.rm = TRUE),
is_hrc = sum(t_mgn, na.rm = TRUE) < 0,
pos_rate = sum(case_ch, na.rm = TRUE) / sum(test_ch, na.rm = TRUE),
wk_date = min(as_of, na.rm = TRUE)) %>%
filter(case_ch_pm > 0, death_ch_pm > 0) %>%
group_by(wk_date) %>%
top_n(n = 3, case_ch_pm) %>%
mutate(wk_dens = mean(dens, na.rm = TRUE)) %>%
ggplot(mapping = aes(x = wk_date, y = case_ch_pm)) +
geom_label(mapping = aes(label = code, fill = reg_2), na.rm = TRUE, position = position_jitter(width = 3, height = 25)) +
geom_col(mapping = aes(y = wk_dens), alpha = .4) +
labs(subtitle = str_c("Five states with most new per capita COVID-19 cases by week through", max(my_data$as_of, na.rm = TRUE), sep = " "),
y = "Weekly new COVID-19 cases per million residents",
x = "Date",
legend = NULL) +
xlim(ymd(20200301), today())## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"
## Joining, by = "code"## `summarise()` regrouping output by 'year_wk', 'reg_2', 'code' (override with `.groups` argument)## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 4 rows containing missing values (geom_col).
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
mutate(year_wk = week(as_of)) %>%
group_by(year_wk, reg_2, code) %>%
summarize(case_ch_pm = 7 * mean(case_ch / pop_2019 * 1000000, na.rm = TRUE),
death_ch_pm = 7 * mean(death_ch / pop_2019 * 1000000, na.rm = TRUE),
is_hrc = sum(t_mgn, na.rm = TRUE) < 0,
pos_rate = sum(case_ch, na.rm = TRUE) / sum(test_ch, na.rm = TRUE),
wk_date = min(as_of, na.rm = TRUE)) %>%
filter(case_ch_pm > 0, death_ch_pm > 0) %>%
group_by(wk_date) %>%
top_n(n = 5, case_ch_pm) %>%
ggplot(mapping = aes(x = wk_date, y = death_ch_pm, label = code, fill = reg_2)) +
geom_label(na.rm = TRUE, position = position_jitter(width = 3, height = 25)) +
theme(legend.position = "none") +
labs(subtitle = str_c("Five states with most per capita COVID-19 deaths by week through", max(my_data$as_of, na.rm = TRUE), sep = " "),
y = "Weekly COVID-19 deaths per million residents",
x = "Date") +
xlim(ymd(20200301), today())## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"## `summarise()` regrouping output by 'year_wk', 'reg_2' (override with `.groups` argument)
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
mutate(year_wk = week(as_of)) %>%
group_by(year_wk, reg_2, code) %>%
summarize(case_ch_pm = 7 * mean(case_ch / pop_2019 * 1000000, na.rm = TRUE),
death_ch_pm = 7 * mean(death_ch / pop_2019 * 1000000, na.rm = TRUE),
is_hrc = sum(t_mgn, na.rm = TRUE) < 0,
pos_rate = sum(case_ch, na.rm = TRUE) / sum(test_ch, na.rm = TRUE),
wk_date = min(as_of, na.rm = TRUE)) %>%
filter(case_ch_pm > 0, death_ch_pm > 0) %>%
group_by(wk_date) %>%
top_n(n = 5, case_ch_pm) %>%
ggplot(mapping = aes(x = wk_date, y = death_ch_pm, label = code, fill = reg_2)) +
geom_label(na.rm = TRUE, position = position_jitter(width = 3, height = 25)) +
theme(legend.position = "none") +
labs(subtitle = str_c("Five states with most per capita COVID-19 deaths by week through", max(my_data$as_of, na.rm = TRUE), sep = " "),
y = "Weekly COVID-19 deaths per million residents",
x = "Date") +
xlim(ymd(20200301), today())## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"## `summarise()` regrouping output by 'year_wk', 'reg_2' (override with `.groups` argument)
my_data %>%
inner_join(code_names) %>%
inner_join(st_pop_2019) %>%
inner_join(results_2016) %>%
inner_join(pop_dens) %>%
mutate(year_wk = week(as_of)) %>%
group_by(year_wk, reg_2, code, dens) %>%
summarize(case_ch_pm = 7 * mean(case_ch / pop_2019 * 1000000, na.rm = TRUE),
death_ch_pm = 7 * mean(death_ch / pop_2019 * 1000000, na.rm = TRUE),
is_hrc = sum(t_mgn, na.rm = TRUE) < 0,
pos_rate = sum(case_ch, na.rm = TRUE) / sum(test_ch, na.rm = TRUE),
wk_date = min(as_of, na.rm = TRUE)) %>%
filter(case_ch_pm > 0, death_ch_pm > 0) %>%
group_by(wk_date) %>%
top_n(n = 7, case_ch_pm) %>%
ggplot(mapping = aes(x = wk_date, y = death_ch_pm, label = code, fill = dens, color = -dens)) +
geom_label(na.rm = TRUE, position = position_jitter(width = 3, height = 25)) +
# theme(legend.position = "none") +
labs(subtitle = str_c("Five states with most per capita COVID-19 deaths by week through", max(my_data$as_of, na.rm = TRUE), sep = " "),
y = "Weekly COVID-19 deaths per million residents",
x = "Date") +
xlim(ymd(20200301), today())## Joining, by = "code"## Joining, by = "name"## Joining, by = "code"
## Joining, by = "code"## `summarise()` regrouping output by 'year_wk', 'reg_2', 'code' (override with `.groups` argument)
Determine weekday factors
wday_fact <- my_data %>%
filter(as_of >= today() - months(1)) %>%
mutate(wk_num = week(as_of),
wkday = lubridate::wday(as_of),
case_ch = if_else(case_ch == 0, 1L, case_ch),
death_ch = if_else(death_ch == 0, 1L, death_ch)) %>%
group_by(code, wk_num) %>%
mutate(wday_case_local = case_ch / mean(case_ch, na.rm = TRUE),
wday_death_local = death_ch / mean(death_ch, na.rm = TRUE)) %>%
ungroup() %>%
group_by(code, wkday) %>%
summarize(wday_case_global = mean(wday_case_local, na.rm = TRUE),
wday_death_global = mean(wday_death_local, na.rm = TRUE))## `summarise()` regrouping output by 'code' (override with `.groups` argument)my_data %>%
inner_join(wday_fact) %>%
inner_join(results_2016) %>%
group_by(code) %>%
mutate(case_ch_adj = wday_case_global * case_ch,
death_ch_adj = wday_death_global * death_ch,
t_day = wkday + 1) %>%
filter(case_ch_adj == max(case_ch_adj, na.rm = TRUE) &
as_of == max(as_of)) %>%
mutate(day_name = lubridate::wday(x = t_day, label = TRUE)) %>%
ggplot(mapping = aes(x = code, y = case_ch_adj)) +
geom_col() +
geom_label(mapping = aes(label = day_name)) +
labs(subtitle = "States with record adjusted new cases reported today")## Joining, by = "code"
## Joining, by = "code"## Warning: Removed 2 rows containing missing values (geom_label).
my_data %>%
inner_join(wday_fact) %>%
inner_join(results_2016) %>%
group_by(code) %>%
mutate(case_ch_adj = wday_case_global * case_ch,
death_ch_adj = wday_death_global * death_ch) %>%
filter(death_ch_adj == max(death_ch_adj, na.rm = TRUE) &
as_of == max(as_of)) %>%
mutate(day_name = lubridate::wday(wkday + 1, label = TRUE)) %>%
ggplot(mapping = aes(x = code, y = death_ch_adj)) +
geom_col() +
geom_label(mapping = aes(label = day_name)) +
labs(subtitle = "States with record adjusted deaths reported today")## Joining, by = "code"
## Joining, by = "code"